# listwise_sampling()
logging.info('================================================================================')
logging.info('dump test file')
# listwise_sampling_test()
logging.info('================================================================================')
logging.info('load query database')
train_queries = Queries.load_from_text('../data/svmlight_train.txt')
valid_queries = Queries.load_from_text('../data/svmlight_val.txt')
test_queries = Queries.load_from_text('../data/svmlight_test.txt')
logging.info('================================================================================')
logging.info('train LambdaMart')
# Prepare metric for this set of queries.
metric = NormalizedDiscountedCumulativeGain(38, queries=[train_queries, valid_queries, test_queries])
# Initialize LambdaMART model and train it.
model = LambdaMART(n_estimators=10000, max_depth=5, shrinkage=0.08, estopping=100, n_jobs=-1, n_iterations=100)
model.fit(metric, train_queries, validation=valid_queries)
logging.info('================================================================================')
logging.info('test')
# Print out the performance on the test set.
logging.info('%s on the test queries: %.8f' % (metric, metric.evaluate_queries(test_queries, model.predict(test_queries, n_jobs=-1))))
#train_queries.save('../data/train_bin')
#valid_queries.save('../data/validation_bin')
#test_queries.save('../data/test_bin')
# ... because loading them will be then faster.
#train_queries = Queries.load('../data/train_bin')
#valid_queries = Queries.load('../data/validation_bin')
#test_queries = Queries.load('../data/test_bin')
logging.info('================================================================================')
# Print basic info about query datasets.
logging.info('Train queries: %s' % train_queries)
logging.info('Valid queries: %s' % valid_queries)
logging.info('Test queries: %s' %test_queries)
logging.info('================================================================================')
# Prepare metric for this set of queries.
metric = NormalizedDiscountedCumulativeGain(38, queries=[train_queries, valid_queries, test_queries])
# Initialize LambdaMART model and train it.
model = LambdaMART(n_estimators=10000, max_depth=4, shrinkage=0.08, estopping=100, n_jobs=-1)
model.fit(metric, train_queries, validation=valid_queries)
logging.info('================================================================================')
# Print out the performance on the test set.
logging.info('%s on the test queries: %.8f' % (metric, metric.evaluate_queries(test_queries, model.predict(test_queries, n_jobs=-1))))
test_queries = Queries.load_from_text('../data/svmlight_test.txt')
logging.info(
'================================================================================'
)
logging.info('train LambdaMart')
# Prepare metric for this set of queries.
metric = NormalizedDiscountedCumulativeGain(
38, queries=[train_queries, valid_queries, test_queries])
# Initialize LambdaMART model and train it.
model = LambdaMART(n_estimators=10000,
max_depth=5,
shrinkage=0.08,
estopping=100,
n_jobs=-1,
n_iterations=100)
model.fit(metric, train_queries, validation=valid_queries)
logging.info(
'================================================================================'
)
logging.info('test')
# Print out the performance on the test set.
logging.info('%s on the test queries: %.8f' %
(metric,
metric.evaluate_queries(test_queries,
model.predict(test_queries, n_jobs=-1))))
class LambdaMART(object):
def __init__(self,
metric='NDCG',
n_estimators=100,
max_depth=None,
max_leaf_nodes=7,
max_features=None,
min_samples_split=2,
min_samples_leaf=1,
shrinkage=0.1,
use_newton_method=True,
use_random_forest=0,
random_thresholds=False,
subsample=1.0,
use_logit_boost=False,
use_ada_boost=False,
estopping=50,
min_n_estimators=1,
base_model=None,
n_jobs=1,
random_state=None):
self.feature_names = None
self.params = {
'metric': metric,
'n_estimators': n_estimators,
'max_depth': max_depth,
'max_leaf_nodes': max_leaf_nodes,
'max_features': max_features,
'min_samples_split': min_samples_split,
'min_samples_leaf': min_samples_leaf,
'shrinkage': shrinkage,
'use_newton_method': use_newton_method,
'use_random_forest': use_random_forest,
'random_thresholds': random_thresholds,
'subsample': subsample,
'use_logit_boost': use_logit_boost,
'use_ada_boost': use_ada_boost,
'estopping': estopping,
'min_n_estimators': min_n_estimators,
'base_model': base_model,
'n_jobs': n_jobs,
'random_state': random_state,
}
def __str__(self):
return self.__repr__()
def __repr(self):
return (
"%s(metric='%s', n_estimators=%d, max_depth=%d, max_leaf_nodes=%d,\n"
"max_features=%d, min_samples_split=%d, min_samples_leaf=%d,\n"
"shrinkage=%f, use_newton_method=%s, use_random_forest=%d,\n"
"random_thresholds=%s, subsample=%f, use_logit_boost=%s, use_ada_boost=%s,\n"
"estopping=%d, min_n_estimators=%d, n_jobs=%d, random_state=%s,\n"
"base_model=%s)" % (
self.__class__.__name__,
self.param["metric"],
self.params["n_estimators"],
self.params["max_depth"],
self.params["max_leaf_nodes"],
self.params["max_features"],
self.params["min_samples_split"],
self.params["min_samples_leaf"],
self.params["shrinkage"],
self.params["use_newton_method"],
self.params["use_random_forest"],
self.params["random_thresholds"],
self.params["subsample"],
self.params["use_logit_boost"],
self.params["use_ada_boost"],
self.params["estopping"],
self.params["min_n_estimators"],
self.params["n_jobs"],
self.params["random_state"],
str(self.params["base_model"]),
))
def _build_query_indptr(self, ids):
"""
The query index pointer into the feature_vectors and relevance_scores
array, i.e. the document feature vectors,
``feature_vectors[query_indptr[i]:query_indptr[i + 1]]``, and the
corresponding relevance scores,
``relevance_scores[query_indptr[i]:query_indptr[i + 1]]``,
are the feature vectors and relevance scores for the i-th
query documents.
"""
query_indptr = [0]
query_ids = []
prev_qid = None
for qid in ids:
if qid == prev_qid:
query_indptr[-1] += 1
else:
query_ids.append(qid)
query_indptr.append(query_indptr[-1] + 1)
prev_qid = qid
return query_indptr, query_ids
def _build_queries(self, X, y, ids, w):
query_indptr, query_ids = self._build_query_indptr(ids)
q = Queries(X, y, query_indptr, query_ids=query_ids)
# weights as per query instead of per-row ... just guess
wn = [
np.mean(w[query_indptr[i]:query_indptr[i + 1]])
for i in range(len(query_indptr) - 1)
]
wn = [w[i] for i in query_indptr[:-1]]
return q, np.ascontiguousarray(wn, dtype='float64')
def fit(self, X, y, ids, weight=None, feature_names=None):
self.feature_names = feature_names
# Unfortunately rankpy only works with integer labels...
# This is far from perfect, but works as a first try
y = (np.asanyarray(y) * 5).astype(np.intc)
# Split out a 10% validation set
splitter = GroupKFold(10)
train, valid = next(splitter.split(X, None, ids))
X_train, X_valid, y_train, y_valid, ids_train, ids_valid, w_train, w_valid = chain.from_iterable(
((a[train], a[valid]) for a in [X, y, ids, weight]))
q_train, w_train = self._build_queries(X_train, y_train, ids_train,
w_train)
q_valid, w_valid = self._build_queries(X_valid, y_valid, ids_valid,
w_valid)
self.model = LambdaMARTModel(**self.params)
self.model.fit(q_train, w_train, q_valid, w_valid)
return self
def predict(self, X, ids, weight, feature_names=None):
self.feature_names = feature_names
query_indptr, query_ids = self._build_query_indptr(ids)
# We wont be using this, but Queries wont instantiate without it
y = np.zeros(X.shape[0])
q = Queries(X, y, query_indptr, query_ids=query_ids)
y_pred = self.model.predict(q, n_jobs=self.params['n_jobs'])
return y_pred
def plot_importance(self):
if self.feature_names is None:
raise Exception('No feature names available')
importance = self.model.feature_importances(self.params['n_jobs'])
# stolen from xgboost
tuples = zip(self.feature_names, importance)
tuples = sorted(tuples, key=lambda x: x[1])
labels, values = tuples
self.save_topn_features(labels, values)
_, ax = plt.subplots(1, 1)
ylocs = np.arange(len(values))
ax.barh(ylocs, values, align='center', height=0.2)
for x, y in zip(values, yloc):
ax.text(x + 1, y, x, va='center')
ax.set_yticks(ylocs)
ax.set_yticklabels(labels)
xlim = (0, max(values) * 1.1)
ax.set_xlim(xlim)
ylim = (-1, len(importance))
ax.set_ylim(ylim)
ax.grid()
return ax
def save_topn_features(self,
labels,
values,
fname="LambdaMART_topn_features.txt",
topn=-1):
if topn == -1:
topn = len(labels)
else:
topn = min(topn, len(labels))
with open(fname, "w") as f:
for i in range(topn):
f.write("%s = %f" % (labels[i], values[i]))
u'hour_of_day_dum22', u'hour_of_day_dum2',
u'prop_starrating_mean_by_prop_id_x',
u'prop_starrating_mean_by_prop_id_y',
u'prop_starrating_median_by_prop_id_y',
u'prop_starrating_median_by_prop_id_x',
u'prop_starrating_std_by_prop_id_y',
u'prop_starrating_std_by_prop_id_x']
d.drop_cols = list(set(d.drop_cols))
X,y,q = d.get_Xyq('train')
q_indptr = q[0:-1] - q[1:]
q_indptr = np.array([0] + [int(i + 1) for i in \
np.where(q_indptr!=0)[0]] + [X.shape[0]])
train_queries = queries.Queries(X, y, q_indptr)
model = LambdaMART(metric='nDCG@38', max_leaf_nodes=7, shrinkage=0.1,
estopping=50, n_jobs=-1, min_samples_leaf=50,
random_state=42)
#model = LambdaRandomForest(metric='nDCG@38', n_estimators=1000)
model.fit(train_queries)
X,y,q = d.get_Xyq('test')
q_indptr = q[0:-1] - q[1:]
q_indptr = np.array([0] + [int(i + 1) for i in \
np.where(q_indptr!=0)[0]] + [X.shape[0]])
test_queries = queries.Queries(X, y, q_indptr, has_sorted_relevances=True)
preds = model.predict(test_queries)
d.test_data['pred_rel'] = preds
result = ndcg_of_df(d.test_data, plus_random=False)
print result
imps = np.argsort(-model.feature_importances())
imps = d.pp_data.drop(d.drop_cols, axis=1).columns[imps]
print imps
